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  <title>源码分析 | ClickHouse和他的朋友们（15）Group By 为什么这么快 | DBKernel - 专注于分享数据库技术</title>
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<p><strong>本文首发于 2021-01-26 21:31:12</strong></p>
<blockquote>
<p>《ClickHouse和他的朋友们》系列文章转载自圈内好友 <a target="_blank" rel="noopener" href="https://bohutang.me/">BohuTANG</a> 的博客，原文链接：<br><a target="_blank" rel="noopener" href="https://bohutang.me/2021/01/21/clickhouse-and-friends-groupby/">https://bohutang.me/2021/01/21/clickhouse-and-friends-groupby/</a><br>以下为正文。</p>
</blockquote>
<p>在揭秘 ClickHouse Group By 之前，先聊聊数据库的性能对比测试问题。</p>
<p>在虎哥看来，一个“讲武德”的性能对比测试应该提供什么信息呢？</p>
<p>首先要尊重客观事实，在什么场景下，x 比 y 快？<br>其次是为什么 x 会比 y 快？</p>
<p>如果以上两条都做到了，还有一点也比较重要： x 的优势可以支撑多久？ 是架构等带来的长期优势，还是一袋烟的优化所得，是否能持续跟上自己的灵魂。</p>
<p>如果只是贴几个妖艳的数字，算不上是 benchmark，而是 benchmarket。</p>
<p>好了，回到 Group By 正题。</p>
<p>相信很多同学已经体验到 ClickHouse Group By 的出色性能，本篇就来分析下快的原因。</p>
<p>首先安慰一下，ClickHouse 的 Group By 并没有使用高大上的黑科技，只是摸索了一条相对较优的方案。</p>
<h2 id="一条-SQL"><a href="#一条-SQL" class="headerlink" title="一条 SQL"></a>一条 SQL</h2><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">SELECT</span> <span class="built_in">sum</span>(number) <span class="keyword">FROM</span> numbers(<span class="number">10</span>) <span class="keyword">GROUP</span> <span class="keyword">BY</span> number <span class="operator">%</span> <span class="number">3</span></span><br></pre></td></tr></table></figure>

<p>我们就以这条简单的 SQL 作为线索，看看 ClickHouse 怎么实现 Group By 聚合。</p>
<h2 id="1-生成-AST"><a href="#1-生成-AST" class="headerlink" title="1. 生成 AST"></a>1. 生成 AST</h2><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br></pre></td><td class="code"><pre><span class="line">EXPLAIN AST</span><br><span class="line"><span class="keyword">SELECT</span> <span class="built_in">sum</span>(number)</span><br><span class="line"><span class="keyword">FROM</span> numbers(<span class="number">10</span>)</span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span> number <span class="operator">%</span> <span class="number">3</span></span><br><span class="line"></span><br><span class="line">┌─explain─────────────────────────────────────┐</span><br><span class="line">│ SelectWithUnionQuery (children <span class="number">1</span>)           │</span><br><span class="line">│  ExpressionList (children <span class="number">1</span>)                │</span><br><span class="line">│   SelectQuery (children <span class="number">3</span>)                  │</span><br><span class="line">│    ExpressionList (children <span class="number">1</span>)              │</span><br><span class="line">│     <span class="keyword">Function</span> <span class="built_in">sum</span> (children <span class="number">1</span>)               │  <span class="operator">/</span><span class="operator">/</span> sum 聚合</span><br><span class="line">│      ExpressionList (children <span class="number">1</span>)            │</span><br><span class="line">│       Identifier number                     │</span><br><span class="line">│    TablesInSelectQuery (children <span class="number">1</span>)         │</span><br><span class="line">│     TablesInSelectQueryElement (children <span class="number">1</span>) │</span><br><span class="line">│      TableExpression (children <span class="number">1</span>)           │</span><br><span class="line">│       <span class="keyword">Function</span> numbers (children <span class="number">1</span>)         │</span><br><span class="line">│        ExpressionList (children <span class="number">1</span>)          │</span><br><span class="line">│         Literal UInt64_10                   │</span><br><span class="line">│    ExpressionList (children <span class="number">1</span>)              │</span><br><span class="line">│     <span class="keyword">Function</span> modulo (children <span class="number">1</span>)            │  <span class="operator">/</span><span class="operator">/</span> number <span class="operator">%</span> <span class="number">3</span> 函数</span><br><span class="line">│      ExpressionList (children <span class="number">2</span>)            │</span><br><span class="line">│       Identifier number                     │</span><br><span class="line">│       Literal UInt64_3                      │</span><br><span class="line">└─────────────────────────────────────────────┘</span><br></pre></td></tr></table></figure>

<h2 id="2-生成-Query-Plan"><a href="#2-生成-Query-Plan" class="headerlink" title="2. 生成 Query Plan"></a>2. 生成 Query Plan</h2><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><span class="line">EXPLAIN</span><br><span class="line"><span class="keyword">SELECT</span> <span class="built_in">sum</span>(number)</span><br><span class="line"><span class="keyword">FROM</span> numbers(<span class="number">10</span>)</span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span> number <span class="operator">%</span> <span class="number">3</span></span><br><span class="line"></span><br><span class="line">┌─explain───────────────────────────────────────────────────────────────────────┐</span><br><span class="line">│ Expression ((Projection <span class="operator">+</span> Before <span class="keyword">ORDER</span> <span class="keyword">BY</span>))                                   │</span><br><span class="line">│   Aggregating                                                                 │ <span class="operator">/</span><span class="operator">/</span> sum 聚合</span><br><span class="line">│     Expression (Before <span class="keyword">GROUP</span> <span class="keyword">BY</span>)                                              │ <span class="operator">/</span><span class="operator">/</span> number <span class="operator">%</span> <span class="number">3</span></span><br><span class="line">│       SettingQuotaAndLimits (<span class="keyword">Set</span> limits <span class="keyword">and</span> quota after reading <span class="keyword">from</span> storage) │</span><br><span class="line">│         ReadFromStorage (SystemNumbers)                                       │</span><br><span class="line">└───────────────────────────────────────────────────────────────────────────────┘</span><br></pre></td></tr></table></figure>

<p>代码主要在 <a target="_blank" rel="noopener" href="https://github.com/ClickHouse/ClickHouse/blob/27ddf78ba572b893cb5351541f566d1080d8a9c6/src/Interpreters/InterpreterSelectQuery.cpp#L1063">InterpreterSelectQuery::executeImpl@Interpreters/InterpreterSelectQuery.cpp</a></p>
<h2 id="3-生成-Pipeline"><a href="#3-生成-Pipeline" class="headerlink" title="3. 生成 Pipeline"></a>3. 生成 Pipeline</h2><figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><span class="line">EXPLAIN PIPELINE</span><br><span class="line"><span class="keyword">SELECT</span> <span class="built_in">sum</span>(number)</span><br><span class="line"><span class="keyword">FROM</span> numbers(<span class="number">10</span>)</span><br><span class="line"><span class="keyword">GROUP</span> <span class="keyword">BY</span> number <span class="operator">%</span> <span class="number">3</span></span><br><span class="line"></span><br><span class="line">┌─explain───────────────────────┐</span><br><span class="line">│ (Expression)                  │</span><br><span class="line">│ ExpressionTransform           │</span><br><span class="line">│   (Aggregating)               │</span><br><span class="line">│   AggregatingTransform        │  <span class="operator">/</span><span class="operator">/</span> sum 计算</span><br><span class="line">│     (Expression)              │</span><br><span class="line">│     ExpressionTransform       │  <span class="operator">/</span><span class="operator">/</span> number <span class="operator">%</span> <span class="number">3</span> 计算</span><br><span class="line">│       (SettingQuotaAndLimits) │</span><br><span class="line">│         (ReadFromStorage)     │</span><br><span class="line">└───────────────────────────────┘</span><br></pre></td></tr></table></figure>

<h2 id="4-执行-Pipeline"><a href="#4-执行-Pipeline" class="headerlink" title="4. 执行 Pipeline"></a>4. 执行 Pipeline</h2><p>Pipeline 是从底部往上逐一执行。</p>
<h3 id="4-1-ReadFromStorage"><a href="#4-1-ReadFromStorage" class="headerlink" title="4.1 ReadFromStorage"></a>4.1 ReadFromStorage</h3><p>首先从 ReadFromStorage 执行，生成一个 block1， 数据如下:</p>
<figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br></pre></td><td class="code"><pre><span class="line">┌─number─┐</span><br><span class="line">│      <span class="number">0</span> │</span><br><span class="line">│      <span class="number">1</span> │</span><br><span class="line">│      <span class="number">2</span> │</span><br><span class="line">│      <span class="number">3</span> │</span><br><span class="line">│      <span class="number">4</span> │</span><br><span class="line">│      <span class="number">5</span> │</span><br><span class="line">│      <span class="number">6</span> │</span><br><span class="line">│      <span class="number">7</span> │</span><br><span class="line">│      <span class="number">8</span> │</span><br><span class="line">│      <span class="number">9</span> │</span><br><span class="line">└────────┘</span><br><span class="line">number类型为 UInt64</span><br></pre></td></tr></table></figure>

<h3 id="4-2-ExpressionTransform"><a href="#4-2-ExpressionTransform" class="headerlink" title="4.2 ExpressionTransform"></a>4.2 ExpressionTransform</h3><p>ExpressionTransform 包含了 2 个 action:</p>
<ol>
<li>名字为 number，type 为 INPUT</li>
<li>名字为 <code>modulo(number, 3)</code>， type 为 FUNCTION</li>
</ol>
<p>经过 ExpressionTransform 运行处理后生成一个新的 block2， 数据如下:</p>
<figure class="highlight sql"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br></pre></td><td class="code"><pre><span class="line">┌─number─┬─modulo(number, <span class="number">3</span>)─┐</span><br><span class="line">│      <span class="number">0</span> │                 <span class="number">0</span> │</span><br><span class="line">│      <span class="number">1</span> │                 <span class="number">1</span> │</span><br><span class="line">│      <span class="number">2</span> │                 <span class="number">2</span> │</span><br><span class="line">│      <span class="number">3</span> │                 <span class="number">0</span> │</span><br><span class="line">│      <span class="number">4</span> │                 <span class="number">1</span> │</span><br><span class="line">│      <span class="number">5</span> │                 <span class="number">2</span> │</span><br><span class="line">│      <span class="number">6</span> │                 <span class="number">0</span> │</span><br><span class="line">│      <span class="number">7</span> │                 <span class="number">1</span> │</span><br><span class="line">│      <span class="number">8</span> │                 <span class="number">2</span> │</span><br><span class="line">│      <span class="number">9</span> │                 <span class="number">0</span> │</span><br><span class="line">└────────┴───────────────────┘</span><br><span class="line">number 类型为 UInt64</span><br><span class="line">modulo(number, <span class="number">3</span>) 类型为 UInt8</span><br></pre></td></tr></table></figure>

<p>代码主要在 <a target="_blank" rel="noopener" href="https://github.com/ClickHouse/ClickHouse/blob/27ddf78ba572b893cb5351541f566d1080d8a9c6/src/Interpreters/ExpressionActions.cpp#L416">ExpressionActions::execute@Interpreters/ExpressionActions.cpp</a></p>
<h3 id="4-3-AggregatingTransform"><a href="#4-3-AggregatingTransform" class="headerlink" title="4.3 AggregatingTransform"></a>4.3 AggregatingTransform</h3><p>AggregatingTransform 是 Group By 高性能的核心所在。<br>本示例中的 <code>modulo(number, 3)</code> 类型为 UInt8，在做优化上，ClickHouse 会选择使用数组代替 hashtable作为分组，区分逻辑见 <a target="_blank" rel="noopener" href="https://github.com/ClickHouse/ClickHouse/blob/27ddf78ba572b893cb5351541f566d1080d8a9c6/src/Interpreters/Aggregator.cpp#L526">Interpreters/Aggregator.cpp</a></p>
<p>在计算 sum 的时候，首先会生成一个数组 [1024]，然后做了一个编译展开(代码 <a target="_blank" rel="noopener" href="https://github.com/ClickHouse/ClickHouse/blob/27ddf78ba572b893cb5351541f566d1080d8a9c6/src/AggregateFunctions/IAggregateFunction.h#L412-L487">addBatchLookupTable8@AggregateFunctions/IAggregateFunction.h</a>):</p>
<figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">static</span> <span class="keyword">constexpr</span> <span class="keyword">size_t</span> UNROLL_COUNT = <span class="number">4</span>;</span><br><span class="line">std::unique_ptr&lt;Data[]&gt; places&#123;<span class="keyword">new</span> Data[<span class="number">256</span> * UNROLL_COUNT]&#125;;</span><br><span class="line"><span class="keyword">bool</span> has_data[<span class="number">256</span> * UNROLL_COUNT]&#123;&#125;; <span class="comment">/// Separate flags array to avoid heavy initialization.</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">size_t</span> i = <span class="number">0</span>;</span><br><span class="line"></span><br><span class="line"><span class="comment">/// Aggregate data into different lookup tables.</span></span><br><span class="line"><span class="keyword">size_t</span> batch_size_unrolled = batch_size / UNROLL_COUNT * UNROLL_COUNT;</span><br><span class="line"><span class="keyword">for</span> (; i &lt; batch_size_unrolled; i += UNROLL_COUNT)</span><br><span class="line">&#123;</span><br><span class="line">    <span class="keyword">for</span> (<span class="keyword">size_t</span> j = <span class="number">0</span>; j &lt; UNROLL_COUNT; ++j)</span><br><span class="line">    &#123;</span><br><span class="line">        <span class="keyword">size_t</span> idx = j * <span class="number">256</span> + key[i + j];</span><br><span class="line">        <span class="keyword">if</span> (<span class="built_in">unlikely</span>(!has_data[idx]))</span><br><span class="line">        &#123;</span><br><span class="line">            <span class="keyword">new</span> (&amp;places[idx]) Data;</span><br><span class="line">            has_data[idx] = <span class="literal">true</span>;</span><br><span class="line">        &#125;</span><br><span class="line">        func.<span class="built_in">add</span>(<span class="keyword">reinterpret_cast</span>&lt;<span class="keyword">char</span> *&gt;(&amp;places[idx]), columns, i + j, <span class="literal">nullptr</span>);</span><br><span class="line">    &#125;</span><br><span class="line">&#125;</span><br></pre></td></tr></table></figure>

<p><code>sum(number) … GROUP BY number % 3</code> 计算方式：</p>
<figure class="highlight plaintext"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><span class="line">array[0] = 0 + 3 + 6 + 9 = 18</span><br><span class="line">array[1] = 1 + 4 + 7 = 12</span><br><span class="line">array[2] = 2 + 5 + 8 = 15</span><br></pre></td></tr></table></figure>

<p>这里只是针对 UInt8 做的一个优化分支，那么对于其他类型怎么优化处理呢？<br>ClickHouse 针对不同的类型分别提供了不同的 hashtable，声势比较浩大（代码见 <a target="_blank" rel="noopener" href="https://github.com/ClickHouse/ClickHouse/blob/27ddf78ba572b893cb5351541f566d1080d8a9c6/src/Interpreters/Aggregator.h#L68-L103">Aggregator.h</a>）：</p>
<figure class="highlight cpp"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">using</span> AggregatedDataWithUInt8Key = FixedImplicitZeroHashMapWithCalculatedSize&lt;UInt8, AggregateDataPtr&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithUInt16Key = FixedImplicitZeroHashMap&lt;UInt16, AggregateDataPtr&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithUInt32Key = HashMap&lt;UInt32, AggregateDataPtr, HashCRC32&lt;UInt32&gt;&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithUInt64Key = HashMap&lt;UInt64, AggregateDataPtr, HashCRC32&lt;UInt64&gt;&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithShortStringKey = StringHashMap&lt;AggregateDataPtr&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithStringKey = HashMapWithSavedHash&lt;StringRef, AggregateDataPtr&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithKeys128 = HashMap&lt;UInt128, AggregateDataPtr, UInt128HashCRC32&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithKeys256 = HashMap&lt;DummyUInt256, AggregateDataPtr, UInt256HashCRC32&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithUInt32KeyTwoLevel = TwoLevelHashMap&lt;UInt32, AggregateDataPtr, HashCRC32&lt;UInt32&gt;&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithUInt64KeyTwoLevel = TwoLevelHashMap&lt;UInt64, AggregateDataPtr, HashCRC32&lt;UInt64&gt;&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithShortStringKeyTwoLevel = TwoLevelStringHashMap&lt;AggregateDataPtr&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithStringKeyTwoLevel = TwoLevelHashMapWithSavedHash&lt;StringRef, AggregateDataPtr&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithKeys128TwoLevel = TwoLevelHashMap&lt;UInt128, AggregateDataPtr, UInt128HashCRC32&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithKeys256TwoLevel = TwoLevelHashMap&lt;DummyUInt256, AggregateDataPtr, UInt256HashCRC32&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithUInt64KeyHash64 = HashMap&lt;UInt64, AggregateDataPtr, DefaultHash&lt;UInt64&gt;&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithStringKeyHash64 = HashMapWithSavedHash&lt;StringRef, AggregateDataPtr, StringRefHash64&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithKeys128Hash64 = HashMap&lt;UInt128, AggregateDataPtr, UInt128Hash&gt;;</span><br><span class="line"><span class="keyword">using</span> AggregatedDataWithKeys256Hash64 = HashMap&lt;DummyUInt256, AggregateDataPtr, UInt256Hash&gt;;</span><br></pre></td></tr></table></figure>

<p>如果我们改成 <code>GROUP BY number*100000</code> 后，它会选择 AggregatedDataWithUInt64Key 的 hashtable 作为分组。</p>
<p>而且 ClickHouse 提供了一种 Two Level 方式，用语应对有大量分组 key 的情况，Level1 先分大组，Level2 小组可以并行计算。</p>
<p>针对 String 类型，根据不同的长度，hashtable 也做了很多优化，代码见 <a target="_blank" rel="noopener" href="https://github.com/ClickHouse/ClickHouse/blob/27ddf78ba572b893cb5351541f566d1080d8a9c6/src/Common/HashTable/StringHashMap.h#L78-L82">HashTable/StringHashMap.h</a></p>
<h2 id="总结"><a href="#总结" class="headerlink" title="总结"></a>总结</h2><p>ClickHouse 会根据 Group By 的最终类型，选择一个最优的 hashtable 或数组，作为分组基础数据结构，使内存和计算尽量最优。</p>
<p>这个”最优解“是怎么找到的？从 test 代码可以看出，是不停的尝试、测试验证出来的，浓厚的 bottom-up 哲学范。</p>
<p>hashtable 测试代码：<a target="_blank" rel="noopener" href="https://github.com/ClickHouse/ClickHouse/tree/27ddf78ba572b893cb5351541f566d1080d8a9c6/src/Interpreters/tests">Interpreters/tests</a></p>
<p>lookuptable 测试代码： <a target="_blank" rel="noopener" href="https://github.com/ClickHouse/ClickHouse/blob/27ddf78ba572b893cb5351541f566d1080d8a9c6/src/Common/tests/average.cpp">tests/average.cpp</a></p>
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